论文标题
线性最小二乘问题的随机梯度下降与部分观察到的数据
Stochastic gradient descent for linear least squares problems with partially observed data
论文作者
论文摘要
我们提出了一种新型的随机梯度下降方法，用于解决部分观察到的数据，以求解线性最小二乘问题。我们的方法使用由随机选择的行和列索引集索引的一键式索引，以在每个步骤更新迭代。提供理论上的融合保证在均等意义上。据报道，数值实验证明了理论发现。
We propose a novel stochastic gradient descent method for solving linear least squares problems with partially observed data. Our method uses submatrices indexed by a randomly selected pair of row and column index sets to update the iterate at each step. Theoretical convergence guarantees in the mean square sense are provided. Numerical experiments are reported to demonstrate the theoretical findings.
